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Artigo de periodico
Brazilian forest dataset: a new dataset to model local biodiversity (2022)
Rios, Ricardo Araújo ; Rios, Tatiane Nogueira; Palma, Gabriel Rodrigues ; Mello, Rodrigo Fernandes de
Source: Journal of Experimental and Theoretical Artificial Intelligence. Unidades: ICMC, ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, BIODIVERSIDADE, FLORESTAS TROPICAIS, MODELAGEM DE DADOS
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ABNT
RIOS, Ricardo Araújo et al. Brazilian forest dataset: a new dataset to model local biodiversity. Journal of Experimental and Theoretical Artificial Intelligence, v. 34, p. 327-354, 2022Tradução . . Disponível em: https://doi.org/10.1080/0952813X.2021.1871972. Acesso em: 30 abr. 2024.
APA
Rios, R. A., Rios, T. N., Palma, G. R., & Mello, R. F. de. (2022). Brazilian forest dataset: a new dataset to model local biodiversity. Journal of Experimental and Theoretical Artificial Intelligence, 34, 327-354. doi:10.1080/0952813X.2021.1871972
NLM
Rios RA, Rios TN, Palma GR, Mello RF de. Brazilian forest dataset: a new dataset to model local biodiversity [Internet]. Journal of Experimental and Theoretical Artificial Intelligence. 2022 ; 34 327-354.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1080/0952813X.2021.1871972
Vancouver
Rios RA, Rios TN, Palma GR, Mello RF de. Brazilian forest dataset: a new dataset to model local biodiversity [Internet]. Journal of Experimental and Theoretical Artificial Intelligence. 2022 ; 34 327-354.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1080/0952813X.2021.1871972
Artigo de periodico
Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII (2021)
Lopes, Tiago José da Silva ; Rios, Ricardo Araújo ; Nogueira, Tatiane ; Mello, Rodrigo Fernandes de
Source: Scientific Reports. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, HEMOFILIA, PROTEÍNAS
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ABNT
LOPES, Tiago José da Silva et al. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII. Scientific Reports, v. 11, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41598-021-92201-3. Acesso em: 30 abr. 2024.
APA
Lopes, T. J. da S., Rios, R. A., Nogueira, T., & Mello, R. F. de. (2021). Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII. Scientific Reports, 11, 1-11. doi:10.1038/s41598-021-92201-3
NLM
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII [Internet]. Scientific Reports. 2021 ; 11 1-11.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1038/s41598-021-92201-3
Vancouver
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII [Internet]. Scientific Reports. 2021 ; 11 1-11.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1038/s41598-021-92201-3
Artigo de periodico
Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series (2021)
Ferreira, Marcos Vinícius dos Santos; Rios, Ricardo Araújo ; Mello, Rodrigo Fernandes de ; Rios, Tatiane Nogueira
Source: Applied Soft Computing. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, FUZZY (INTELIGÊNCIA ARTIFICIAL)
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ABNT
FERREIRA, Marcos Vinícius dos Santos et al. Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series. Applied Soft Computing, v. 113, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2021.108011. Acesso em: 30 abr. 2024.
APA
Ferreira, M. V. dos S., Rios, R. A., Mello, R. F. de, & Rios, T. N. (2021). Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series. Applied Soft Computing, 113, 1-13. doi:10.1016/j.asoc.2021.108011
NLM
Ferreira MV dos S, Rios RA, Mello RF de, Rios TN. Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series [Internet]. Applied Soft Computing. 2021 ; 113 1-13.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.asoc.2021.108011
Vancouver
Ferreira MV dos S, Rios RA, Mello RF de, Rios TN. Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series [Internet]. Applied Soft Computing. 2021 ; 113 1-13.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.asoc.2021.108011
Artigo de periodico
Prediction of hemophilia A severity using a small-input machine-learning framework (2021)
Lopes, Tiago José da Silva ; Rios, Ricardo Araújo ; Nogueira, Tatiane ; Mello, Rodrigo Fernandes de
Source: npj Systems Biology and Applications. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, HEMOFILIA, PROTEÍNAS
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ABNT
LOPES, Tiago José da Silva et al. Prediction of hemophilia A severity using a small-input machine-learning framework. npj Systems Biology and Applications, v. 7, p. 1-8, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41540-021-00183-9. Acesso em: 30 abr. 2024.
APA
Lopes, T. J. da S., Rios, R. A., Nogueira, T., & Mello, R. F. de. (2021). Prediction of hemophilia A severity using a small-input machine-learning framework. npj Systems Biology and Applications, 7, 1-8. doi:10.1038/s41540-021-00183-9
NLM
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Prediction of hemophilia A severity using a small-input machine-learning framework [Internet]. npj Systems Biology and Applications. 2021 ; 7 1-8.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1038/s41540-021-00183-9
Vancouver
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Prediction of hemophilia A severity using a small-input machine-learning framework [Internet]. npj Systems Biology and Applications. 2021 ; 7 1-8.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1038/s41540-021-00183-9
Artigo de periodico
Country transition index based on hierarchical clustering to predict next COVID-19 waves (2021)
Rios, Ricardo Araújo ; Nogueira, Tatiane ; Coimbra, Danilo Barbosa; Lopes, Tiago José da Silva ; Abraham, Ajith ; Mello, Rodrigo Fernandes de
Source: Scientific Reports. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, INTELIGÊNCIA ARTIFICIAL, SURTOS DE DOENÇAS, COVID-19
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ABNT
RIOS, Ricardo Araújo et al. Country transition index based on hierarchical clustering to predict next COVID-19 waves. Scientific Reports, v. 11, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41598-021-94661-z. Acesso em: 30 abr. 2024.
APA
Rios, R. A., Nogueira, T., Coimbra, D. B., Lopes, T. J. da S., Abraham, A., & Mello, R. F. de. (2021). Country transition index based on hierarchical clustering to predict next COVID-19 waves. Scientific Reports, 11, 1-13. doi:10.1038/s41598-021-94661-z
NLM
Rios RA, Nogueira T, Coimbra DB, Lopes TJ da S, Abraham A, Mello RF de. Country transition index based on hierarchical clustering to predict next COVID-19 waves [Internet]. Scientific Reports. 2021 ; 11 1-13.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1038/s41598-021-94661-z
Vancouver
Rios RA, Nogueira T, Coimbra DB, Lopes TJ da S, Abraham A, Mello RF de. Country transition index based on hierarchical clustering to predict next COVID-19 waves [Internet]. Scientific Reports. 2021 ; 11 1-13.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1038/s41598-021-94661-z
Artigo de periodico
Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification (2020)
Canário, João Paulo ; Mello, Rodrigo Fernandes de ; Curilem, Millaray ; Huenupan, Fernando ; Rios, Ricardo Araújo
Source: Data in Brief. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, REDES NEURAIS, VULCÕES
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ABNT
CANÁRIO, João Paulo et al. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification. Data in Brief, v. 30, p. 1-6, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.dib.2020.105627. Acesso em: 30 abr. 2024.
APA
Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification. Data in Brief, 30, 1-6. doi:10.1016/j.dib.2020.105627
NLM
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Data in Brief. 2020 ; 30 1-6.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.dib.2020.105627
Vancouver
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Data in Brief. 2020 ; 30 1-6.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.dib.2020.105627
Artigo de periodico
In-depth comparison of deep artificial neural network architectures on seismic events classification (2020)
Canário, João Paulo ; Mello, Rodrigo Fernandes de ; Curilem, Millaray ; Huenupan, Fernando ; Rios, Ricardo Araújo
Source: Journal of Volcanology and Geothermal Research. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, REDES NEURAIS, VULCÕES, ANÁLISE DE ONDALETAS
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ABNT
CANÁRIO, João Paulo et al. In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, v. 401, p. Se 2020, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.jvolgeores.2020.106881. Acesso em: 30 abr. 2024.
APA
Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, 401, Se 2020. doi:10.1016/j.jvolgeores.2020.106881
NLM
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ; 401 Se 2020.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881
Vancouver
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ; 401 Se 2020.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881
Artigo de periodico
Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations (2018)
Curilem, Millaray; Mello, Rodrigo Fernandes de; Huenupan, Fernando; San Martin, Cesar; Franco, Luis; Hernández, Erasmo; Rios, Ricardo Araújo
Source: Journal of Volcanology and Geothermal Research. Unidade: ICMC
Subjects: VULCÕES, ONDAS SÍSMICAS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES
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ABNT
CURILEM, Millaray et al. Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations. Journal of Volcanology and Geothermal Research, v. No 2018, p. 63-78, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.jvolgeores.2018.10.023. Acesso em: 30 abr. 2024.
APA
Curilem, M., Mello, R. F. de, Huenupan, F., San Martin, C., Franco, L., Hernández, E., & Rios, R. A. (2018). Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations. Journal of Volcanology and Geothermal Research, No 2018, 63-78. doi:10.1016/j.jvolgeores.2018.10.023
NLM
Curilem M, Mello RF de, Huenupan F, San Martin C, Franco L, Hernández E, Rios RA. Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations [Internet]. Journal of Volcanology and Geothermal Research. 2018 ; No 2018 63-78.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.jvolgeores.2018.10.023
Vancouver
Curilem M, Mello RF de, Huenupan F, San Martin C, Franco L, Hernández E, Rios RA. Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations [Internet]. Journal of Volcanology and Geothermal Research. 2018 ; No 2018 63-78.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.jvolgeores.2018.10.023
Artigo de periodico
Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals (2016)
Rios, Ricardo Araújo; Mello, Rodrigo Fernandes de
Source: Signal Processing. Unidade: ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIOS, Ricardo Araújo e MELLO, Rodrigo Fernandes de. Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals. Signal Processing, v. 118, n. Ja 2016, p. 159-176, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.sigpro.2015.07.003. Acesso em: 30 abr. 2024.
APA
Rios, R. A., & Mello, R. F. de. (2016). Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals. Signal Processing, 118( Ja 2016), 159-176. doi:10.1016/j.sigpro.2015.07.003
NLM
Rios RA, Mello RF de. Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals [Internet]. Signal Processing. 2016 ; 118( Ja 2016): 159-176.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.sigpro.2015.07.003
Vancouver
Rios RA, Mello RF de. Applying Empirical Mode Decomposition and mutual information to separate stochastic and deterministic influences embedded in signals [Internet]. Signal Processing. 2016 ; 118( Ja 2016): 159-176.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.sigpro.2015.07.003
Artigo de periodico
Estimating determinism rates to detect patterns in geospatial datasets (2015)
Rios, Ricardo Araújo; Parrott, Lael; Lange, Holger; Mello, Rodrigo Fernandes de
Source: Remote Sensing of Environment. Unidade: ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE, ANÁLISE DE SÉRIES TEMPORAIS, VEGETAÇÃO
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ABNT
RIOS, Ricardo Araújo et al. Estimating determinism rates to detect patterns in geospatial datasets. Remote Sensing of Environment, v. 156, n. ja 2015, p. 11-20, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2014.09.019. Acesso em: 30 abr. 2024.
APA
Rios, R. A., Parrott, L., Lange, H., & Mello, R. F. de. (2015). Estimating determinism rates to detect patterns in geospatial datasets. Remote Sensing of Environment, 156( ja 2015), 11-20. doi:10.1016/j.rse.2014.09.019
NLM
Rios RA, Parrott L, Lange H, Mello RF de. Estimating determinism rates to detect patterns in geospatial datasets [Internet]. Remote Sensing of Environment. 2015 ; 156( ja 2015): 11-20.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.rse.2014.09.019
Vancouver
Rios RA, Parrott L, Lange H, Mello RF de. Estimating determinism rates to detect patterns in geospatial datasets [Internet]. Remote Sensing of Environment. 2015 ; 156( ja 2015): 11-20.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.rse.2014.09.019
Artigo de periodico
Testing for linear and nonlinear Gaussian processes in nonstationary time series (2015)
Rios, Ricardo Araújo; Small, Michael; Mello, Rodrigo Fernandes de
Source: International Journal of Bifurcation and Chaos. Unidade: ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
RIOS, Ricardo Araújo e SMALL, Michael e MELLO, Rodrigo Fernandes de. Testing for linear and nonlinear Gaussian processes in nonstationary time series. International Journal of Bifurcation and Chaos, v. 25, n. 1, p. 1550013-1-1550013-19, 2015Tradução . . Disponível em: https://doi.org/10.1142/S0218127415500133. Acesso em: 30 abr. 2024.
APA
Rios, R. A., Small, M., & Mello, R. F. de. (2015). Testing for linear and nonlinear Gaussian processes in nonstationary time series. International Journal of Bifurcation and Chaos, 25( 1), 1550013-1-1550013-19. doi:10.1142/S0218127415500133
NLM
Rios RA, Small M, Mello RF de. Testing for linear and nonlinear Gaussian processes in nonstationary time series [Internet]. International Journal of Bifurcation and Chaos. 2015 ; 25( 1): 1550013-1-1550013-19.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1142/S0218127415500133
Vancouver
Rios RA, Small M, Mello RF de. Testing for linear and nonlinear Gaussian processes in nonstationary time series [Internet]. International Journal of Bifurcation and Chaos. 2015 ; 25( 1): 1550013-1-1550013-19.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1142/S0218127415500133
Tese (Doutorado)
Improving time series modeling by decomposing and analysing stochastic and deterministic influences (2013)
Rios, Ricardo Araújo; Mello, Rodrigo Fernandes de (Orientador)
Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, APRENDIZADO COMPUTACIONAL, SISTEMAS DINÂMICOS, SISTEMAS DISTRIBUÍDOS, SISTEMAS AUTÔNOMOS, COMPUTAÇÃO BIOINSPIRADA
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ABNT
RIOS, Ricardo Araújo. Improving time series modeling by decomposing and analysing stochastic and deterministic influences. 2013. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2013. Disponível em: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112013-143708/. Acesso em: 30 abr. 2024.
APA
Rios, R. A. (2013). Improving time series modeling by decomposing and analysing stochastic and deterministic influences (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112013-143708/
NLM
Rios RA. Improving time series modeling by decomposing and analysing stochastic and deterministic influences [Internet]. 2013 ;[citado 2024 abr. 30 ] Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112013-143708/
Vancouver
Rios RA. Improving time series modeling by decomposing and analysing stochastic and deterministic influences [Internet]. 2013 ;[citado 2024 abr. 30 ] Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112013-143708/
Artigo de periodico
Improving time series modeling by decomposing and analyzing stochastic and deterministic influences (2013)
Rios, Ricardo Araújo; Mello, Rodrigo Fernandes de
Source: Signal Processing. Unidade: ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIOS, Ricardo Araújo e MELLO, Rodrigo Fernandes de. Improving time series modeling by decomposing and analyzing stochastic and deterministic influences. Signal Processing, v. no 2013, n. 11, p. 3001-3013, 2013Tradução . . Disponível em: https://doi.org/10.1016/j.sigpro.2013.04.017. Acesso em: 30 abr. 2024.
APA
Rios, R. A., & Mello, R. F. de. (2013). Improving time series modeling by decomposing and analyzing stochastic and deterministic influences. Signal Processing, no 2013( 11), 3001-3013. doi:10.1016/j.sigpro.2013.04.017
NLM
Rios RA, Mello RF de. Improving time series modeling by decomposing and analyzing stochastic and deterministic influences [Internet]. Signal Processing. 2013 ; no 2013( 11): 3001-3013.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.sigpro.2013.04.017
Vancouver
Rios RA, Mello RF de. Improving time series modeling by decomposing and analyzing stochastic and deterministic influences [Internet]. Signal Processing. 2013 ; no 2013( 11): 3001-3013.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1016/j.sigpro.2013.04.017
Artigo de periodico
A systematic literature review on decomposition approaches to estimate time series components (2012)
Rios, Ricardo Araújo; Mello, Rodrigo Fernandes de
Source: INFOCOMP : Journal of Computer Science. Unidade: ICMC
Subjects: PROGRAMAÇÃO CONCORRENTE, SISTEMAS DISTRIBUÍDOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIOS, Ricardo Araújo e MELLO, Rodrigo Fernandes de. A systematic literature review on decomposition approaches to estimate time series components. INFOCOMP : Journal of Computer Science, v. 11, n. 3-4, p. 31-46, 2012Tradução . . Disponível em: http://www.dcc.ufla.br/infocomp/images/artigos/v11.3/art05.pdf. Acesso em: 30 abr. 2024.
APA
Rios, R. A., & Mello, R. F. de. (2012). A systematic literature review on decomposition approaches to estimate time series components. INFOCOMP : Journal of Computer Science, 11( 3-4), 31-46. Recuperado de http://www.dcc.ufla.br/infocomp/images/artigos/v11.3/art05.pdf
NLM
Rios RA, Mello RF de. A systematic literature review on decomposition approaches to estimate time series components [Internet]. INFOCOMP : Journal of Computer Science. 2012 ; 11( 3-4): 31-46.[citado 2024 abr. 30 ] Available from: http://www.dcc.ufla.br/infocomp/images/artigos/v11.3/art05.pdf
Vancouver
Rios RA, Mello RF de. A systematic literature review on decomposition approaches to estimate time series components [Internet]. INFOCOMP : Journal of Computer Science. 2012 ; 11( 3-4): 31-46.[citado 2024 abr. 30 ] Available from: http://www.dcc.ufla.br/infocomp/images/artigos/v11.3/art05.pdf
Trabalho de evento
Analysis of decomposition techniques to estimate time series stochastic and deterministic components: a systematic review (2011)
Rios, Ricardo Araújo; Mello, Rodrigo Fernandes de
Source: Anais. Conference titles: Escola de Séries Temporais e Econometria - ESTE. Unidade: ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIOS, Ricardo Araújo e MELLO, Rodrigo Fernandes de. Analysis of decomposition techniques to estimate time series stochastic and deterministic components: a systematic review. 2011, Anais.. São Paulo: ABE, 2011. . Acesso em: 30 abr. 2024.
APA
Rios, R. A., & Mello, R. F. de. (2011). Analysis of decomposition techniques to estimate time series stochastic and deterministic components: a systematic review. In Anais. São Paulo: ABE.
NLM
Rios RA, Mello RF de. Analysis of decomposition techniques to estimate time series stochastic and deterministic components: a systematic review. Anais. 2011 ;[citado 2024 abr. 30 ]
Vancouver
Rios RA, Mello RF de. Analysis of decomposition techniques to estimate time series stochastic and deterministic components: a systematic review. Anais. 2011 ;[citado 2024 abr. 30 ]
Trabalho de evento
An empirical approach using spectral and recurrence analysis to estimate time series stochastic and deterministic components (2011)
Rios, Ricardo Araújo; Mello, Rodrigo Fernandes de
Source: Anais. Conference titles: Conferência Brasileira de Dinâmica, Controle e Aplicações - DINCON. Unidade: ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIOS, Ricardo Araújo e MELLO, Rodrigo Fernandes de. An empirical approach using spectral and recurrence analysis to estimate time series stochastic and deterministic components. 2011, Anais.. São Carlos: SBMAC, 2011. . Acesso em: 30 abr. 2024.
APA
Rios, R. A., & Mello, R. F. de. (2011). An empirical approach using spectral and recurrence analysis to estimate time series stochastic and deterministic components. In Anais. São Carlos: SBMAC.
NLM
Rios RA, Mello RF de. An empirical approach using spectral and recurrence analysis to estimate time series stochastic and deterministic components. Anais. 2011 ;[citado 2024 abr. 30 ]
Vancouver
Rios RA, Mello RF de. An empirical approach using spectral and recurrence analysis to estimate time series stochastic and deterministic components. Anais. 2011 ;[citado 2024 abr. 30 ]

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